Machine learning is a pretty hot trending field and you don’t want to miss the train, right? Today, let’s look at how you can become a machine learning expert.
I have some decent knowledge of machine learning since I learned it to do my final year project at college.
From my little experience and knowledge, I want to give you a good and specific plan to master machine learning. After the end of reading this article, you should have a definite study plan for a successful future.
So, let’s start by quickly mentioning what is machine learning.
Machine learning is all about making the computer to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots of data.
Here is a link to one of my articles in case you need to understand what is machine learning in a simpler way. So, let’s come to the business. How to become an expert in machine learning? There are mainly two approaches. The “Top-Down” approach and the “Bottom-Up” approach. Let’s discuss them in detail and see which one is the best approach for you.
Bottom-Up Learning Approach
Let’s see what is the bottom-up approach. The bottom-up approach means you need to first learn the theoretical fundamentals of machine learning. This includes learning statistics, maths, and programming.
If you want to try this route, then you first need to learn the fundamentals of maths, statistics and basic programming concepts of a programming language, especially either Python or R. Then, you can start building projects and have some fun.
I have explained the top 5 subjects that you need to master in order to become a machine learning expert, along with some resources that may help you learn those subjects easily.
Let’s see what are those skills that will make you a good machine learning engineer.
1. Programming Skills
You don’t need to be an expert in programming skills, but it is preferable to know data structures, algorithms and object-oriented concepts in programming.
The most popular programming languages to implement machine learning are Python and R.
I suggest you learn Python since it is very simpler and popular. Python provides a lot of in-built functionalities to implement your machine learning projects easily. Also, there is a huge open source community for Python.
If you want to learn the basics of Python programming language, I have a tutorial that covers the basics of Python. Check that out here.
Become a master in the programming language of your choice and understand a little bit about other programming languages as well.
2. Math Skills
You need to have basic knowledge of mathematics in order to become a machine learning expert.
Many of the machine learning algorithms implement a lot of mathematical concepts. So, you need to have a fair idea about them while you are coding.
Under mathematics, specifically, you need to learn probability and statistics, linear algebra, calculus, etc.
Probability and Statistics
Machine learning is very much closely related to statistics. You need to know the basic fundamentals of probability and statistics, descriptive statistics, Bayes rule and random variables, probability distributions, sampling, hypothesis testing, regression, and decision analysis.
You need to learn how to work with matrices and basic operations on matrices such as matrix addition, matrix subtraction, scalar, and vector multiplication, inverse, transpose, and vector spaces.
In calculus, you need to know the basics of differential and integral calculus.
All the mathematical topics that I have listed may not be even needed for you in your projects. But having a deep understanding of the underlying concepts will be helpful and enjoyable while working.
For learning these basics, you can try the Khan Academy courses. they have some amazing learning materials on Linear algebra and statistics.
3. Data Engineer Skills
You need to have the ability to work with a large amount of data (big data), data preprocessing, knowledge of SQL and NoSQL, ETL (Extract Transform and Load) operations, data analysis and visualizations skills.
Understand the fundamental data engineering concepts so that you can implement them in your projects.
4. Knowledge of Machine Learning Algorithms
You should be familiar with popular machine learning algorithms such as linear regression, logistic regression, decision tree, random forest, clustering (K means, hierarchical), reinforcement learning and neural networks.
You should have some great knowledge about these algorithms so that you can implement them easily in a programming language like Python. Python provides many useful modules, standard libraries and methods to easily implement these algorithms in your projects.
5. Knowledge of Machine Learning Frameworks
You should be familiar with popular machine learning frameworks such as Scikit learn, Tensorflow, Azure, Caffe, Theano, Spark, and Torch.
These are some of the famous machine learning frameworks. If you are starting with Python, you can try keras, scikit-learn or tensorflow.
Keras is a bit more beginner-friendly framework. You can use that to create your machine learning models easily.
So, these are the overall things that you should master in order to become a machine learning expert.
Now, let’s see what is the top-down approach to learning machine learning.
Top-Down Learning Approach
The top-down approach means you can start coding the machine learning models and learn about the underlying concepts as you go along the journey.
That seems like the easiest route, right? So, if you want to take that route, then start with learning Python and then directly start coding your projects.
I will tell you more specifically. Learn the fundamentals of Python. Then, you need to pick a project to do. Then start coding that project and learn the theories as you do the project.
To learn with this approach, I will recommend you to check out Fast.ai. They have some great resources to help you learn machine learning by coding.
If you don’t know what project to choose, there are tons of projects available online. You can learn from online tutorials and do projects.
Here is one project for you. I have done a project to classify images of dogs and cats using Python. I made a simple tutorial on this project. Check it out here and do that project for yourself.
You can find many similar tutorials to help you do simple machine learning projects.
So, from the two methods, I will strongly recommend taking the top-down route. That will be more practical, more fun and engaging. Later on, you can learn all the boring theories in the world as you want. However, going the bottom-up route is fine too.
This is how you can become a machine learning expert. Don’t just read the article and procrastinate like failed people. Decide your learning path right now.
Think and find out what are the actionable steps that you need to take to achieve your goals. Write them down somewhere. Start learning.
Successful people do not procrastinate. They will start doing the right things.
Here are my special ninja tips for you. Immerse yourself in machine learning by these tips:
- Subscribe and watch machine learning related Youtube channels and read machine learning blogs.
- Make friendships with people who are learning machine learning or working in the machine learning field.
- Attend workshops, technical talks, and conferences related to machine learning.
I hope you got a clear idea about the skills you must have to go through a successful machine learning journey.
I hope that the learning paths and resources that I have mentioned in this article will help you to become a great machine learning expert.
If you want me to add something more to this article, do let me know in the comments. Also, if you have any doubts or queries, mention them in the comments section down below. I will be happy to help.
If this article was helpful for you, do share it with your friends so that someone else will also get help from this article.